Finally, it is worth pointing out that
PromptGen not only offers generality for algorithmic design and modularity for control composition, but also enables iterative controls when some controls are contingent on others.
For instance, one may train a text-controlled distribution and then de-bias this distribution.
To achieve this, we view the composition of the INN and the generative model as a new “generative model” to be controlled.
Figure REF (f) illustrates that PromptGen reveals “reporting bias” of the CLIP model [1]}, where “without makeup” is – perhaps surprisingly – positively correlated with female, and Figure REF (c) shows that PromptGen can further mitigate this bias with iterative control.
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